Robust, Compliant Assembly via Optimal Belief Space Planning

In automated manufacturing, robots must reliably assemble parts of various geometries and low tolerances. Ideally, they plan the required motions autonomously. This poses a substantial challenge due to high-dimensional state spaces and non-linear contact-dynamics. Furthermore, object poses and model...

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Bibliographic Details
Main Authors: Wirnshofer, Florian, Schmitt, Philipp S., Feiten, Wendelin, Wichert, Georg V., Burgard, Wolfram
Format: Conference Proceeding
Language:English
Subjects:
Aerospace electronics
Dynamics
Planning
Robots
Task analysis
Trajectory
Uncertainty
Online Access:Request full text
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Summary:In automated manufacturing, robots must reliably assemble parts of various geometries and low tolerances. Ideally, they plan the required motions autonomously. This poses a substantial challenge due to high-dimensional state spaces and non-linear contact-dynamics. Furthermore, object poses and model parameters, such as friction, are not exactly known and a source of uncertainty. The method proposed in this paper models the task of parts assembly as a belief space planning problem over an underlying impedance-controlled, compliant system. To solve this planning problem we introduce an asymptotically optimal belief space planner by extending an optimal, randomized, kinodynamic motion planner to nondeterministic domains. Under an expansiveness assumption we establish probabilistic completeness and asymptotic optimality. We validate our approach in thorough, simulated and realworld experiments of multiple assembly tasks. The experiments demonstrate our planner's ability to reliably assemble objects, solely based on CAD models as input.
ISSN:2577-087X
DOI:10.1109/ICRA.2018.8460995